Interpolating scattered data using 2D self-organizing feature maps

Many computer-aided design, computer graphics, and data visualization applications require freeform surfaces to be created from irregularly spaced and unorganized digitized data. Most surface interpolation and approximation techniques require information about the connectivity between these measured points. In contrast, the scattered data interpolation method described in this paper exploits the topological structure and unsupervised learning algorithm of a 2D self-organizing feature map (SOFM) to iteratively create a polygonal surface mesh that takes the general shape of the underlying object. The mesh representation, with quadrilateral elements, can be used to produce a facetted surface model for direct visualization or provide the means to "parametrize" the scattered data prior to generating a smooth continuous surface. Several illustrative examples using scattered range data are provided to demonstrate the data interpolation and surface reconstruction capability of the proposed 2D SOFM. (C) 2003 Published by Elsevier Inc.